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Geo-Spatial Distribution of Frequencies of MTB/RIF Detected Specimens Based on Requesting Health Facilities in Manicaland Zimbabwe for 2017 and 2018

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Geo-Spatial Distribution of Frequencies of MTB/RIF Detected Specimens Based on Requesting Health Facilities in Manicaland Zimbabwe for 2017 and 2018 Medical Journal of Zambia, Vol. 48 (2): 78 - 84 (2021) Original Article Geo-Spatial Distribution of Frequencies of MTB/RIF Detected Specimens based on Requesting Health Facilities in Manicaland Zimbabwe for 2017 and 2018 K Zvinoera 1, J Mutsvangwa2, E Chikaka1, T D Coutinho 3, V Kampira 4, S Mharakurwa1 1. Department of Health Sciences, College of Health Agriculture and Natural Sciences Africa University, Zimbabwe 2. Biomedical Research and Training Institute, Harare Zimbabwe 3. Department of Geography and Environmental Science University of Zimbabwe 4. Mutare Provincial Hospital, Ministry of Health Zimbabwe ABSTRACT Genexpert CPU crushed. Geographical Positioning System (GPS) of the health facilities were Objectives: The aim of this study was to produce recorded.The study used MTB detected frequencies Geo Spatial Distribution of Frequencies of at a facility in relation to surrounding facilities in MTB/RIF Detected Specimens based on Requesting Manicaland, then ran optimised hotspot analysis Health Facilities in Manicaland Zimbabwe for 2017 function in Arc Map 10.5 to implement the Gi* and 2018, so as to give insight to TB program statistic. managers. Focusing elimination interventions on hot pockets of Tuberculosis (TB) strengthens Results: Overall provincial MTB detected rationale use of resources in resource limited positivity was 2221/36055 (6.2%).Overall countries like Zimbabwe. Early detection and early provincial Rifampicin Resistant (RR) positivity was treatment is backbone of breaking TB transmission. .111.2221(5.0%).Geo-spatial map of Manicaland Drug resistant tuberculosis (DRTB) control showed 10 facilities that are RR hotspots with 7/10 interventions like Programmatic Management of (70%) of the facilities in Buhera district. Chipinge Drug Resistant TB or mentoring on Short, all Oral district had facilities that were MTB detected high Regimen for Rifampicin resistant Tuberculosis hotspots.For the whole of Manicaland, Buhera (ShORRT) will be driven by science. district had100% MTB detected low hotspots facilities. Ninety percent hotspots were clustered Materials and Methods: The retrospective study around 2 of the 15 Genexpert Sites in Manicaland, was carried out in Manicaland, Zimbabwe. namely Murambinda Mission Hospital and Manicaland one of the 10 provinces in Zimbabwe, Chipinge District Hospital. has 7 districts with 308 health facilities. During this retrospective cross sectional study 2221 MTB Conclusion: Study identified health facilities with detected results of 2017 and 2018, downloaded from high frequencies of RR areas. For the identified 14 of the 15 Genexpert sites in Manicaland were health facilities with high frequencies of RR employed to generate hotspot maps. Fifteenth specimens, NTP may focus DRTB control Genexpert site lost its electronic records when interventions like PMDT, or mentoring on ShORRT. For the health facilities with high frequencies of Corresponding author: MTB detected NTP can focus trainings in TB Case Zvinoera Katherine Cell phone +263777398293 Key Words: Genexpert MTB/Rif, Geographical Positioning Email- [email protected] System, Geo Spatial Mapping 78 Medical Journal of Zambia, Vol. 48 (2): 78 - 84 (2021) Management. Instead of uniformly spreading the rationale prepositioning of resources, a must in limited resources to all 325 facilities, efforts resource limited countries like Zimbabwe. streamlined to manageable number of 20 facilities in Resources like TB case management training, commensurate with identified gap( e.g. objective Programmatic Multi Drug Resistant Tuberculosis selection of cadres for training, data driven training, Mentoring through on site supportive supportive supervision & targeted awareness visits, community awareness campaigns in targeted campaigns). communities7. Geo Spatial Distribution of Frequencies of MTB/RIF Detected Specimens INTRODUCTION based on Requesting Health Facilities in Utilization of geo-spatial mapping enables Manicaland had not been created, judging by the assessing of geographical distribution of infectious lack of literature. There was need to generate geo- diseases. Tuberculosis (TB)like other infectious spatial distribution mapping of Genexpert diseases, have epidemiological patterns that differ MTB/Rif's semi quantitative results in Manicaland. across geographical regions.1,2,3Early TB detection Question on hand was what the Geo Spatial and early treatment is the backbone to breaking Distribution of Frequencies of MTB is/RIF transmission. In attempts to manage TB, the World Detected Specimens based on Requesting Health Health Organization (W.H.O.)'s End TB Strategy, Facilities in Manicaland Zimbabwe for 2017 and has a goal to end the TB epidemic by reduction to 2018? The aim of this study was to generate geo- <10 cases per 100 000 population by 2035.4 spatial distribution maps of Genexpert MTB/Rif Zimbabwe is among the 14 countries with a triple results in Manicaland, Zimbabwe so as to give burden of TB, Human Immunodeficiency Virus insight to TB program managers. In the study a positive people co-infected with TB (TB/HIV) and spatial map was generated showing the Geo Spatial multi-drug resistant TB (MDR-TB). Incidence of Distribution of Frequencies of MTB/RIF TB in Zimbabwe in 2018 was estimated to be 210 Detected Specimens based on Requesting Health per 100,000 population. Further estimates were that Facilities in Manicaland Zimbabwe for 2017 and 62% of patients diagnosed TB, were HIV co- 2018. Focusing elimination interventions on hot infected. Lastly among multi drug resistant TB pockets of Tuberculosis (TB) strengthens rationale (MDR-TB) patients HIV prevalence was 80%5. use of resources in resource limited countries like Zimbabwe. Early detection and early treatment is To end TB successfully, disease elimination backbone of breaking TB transmission. Drug campaigns need to be characterized by locally resistant tuberculosis (DRTB) control interventions tailored responses that are informed by evidence like Programmatic Management of Drug Resistant based medicine(e.g. objective selection of cadres for TB or mentoring on Short, all Oral Regimen for training, data driven supportive supervision & Rifampicin resistant Tuberculosis (ShORRT) will targeted awareness campaigns).For such a response be driven by science. to tuberculosis, a three-step process is vital, which includes, using existing programmatic data to guide MATERIALS AND METHODS decisions, collection of additional data (e.g. geographic information, drug resistance, and risk The study design employed was retrospective cross factors) to aid creation of tailored responses. Geo- sectional design. The study was carried out in Spatial distribution maps improve understanding of Manicaland Province (figure1). Figure 1 has TB transmission dynamics. Focus on eliminating Zimbabwe map on top left corner, with Manicaland hot spots of TB is one strategy to meet the WHO end located on the Eastern part of Zimbabwe. Figure 1 TB target of zero death due to TB by 20356. has enlarged map of Manicaland, which covers a Decisions made from spatial mapping include total area of 36,459 square kilometres. It is the 79 Medical Journal of Zambia, Vol. 48 (2): 78 - 84 (2021) second most populous province after Harare with a MTB detected very low, MTB detected trace. Each population total of 1.75million (Census, 2012). It is followed by rifampicin resistant result (RR); RR not the third most densely populated province after detected RR detected or RR indeterminate. Harare and Bulawayo. According to Zimbabwe District Health Information System (DHIS2) the The geographic locations of health facilities were province has 308 health facilities in 7 districts collected using Google earth pro. The geographic served by 15 Genexpert sites. The study population coordinates for Manicaland health facilities were was MTB/Rif results analysed in Manicaland for the then entered in a spread sheet and converted into a period 2017 and 2018. Complete enumeration was comma-separated values (csv) file for use in a GIS used. Study data sources were Laboratory TB environment. The points were mapped using arcGis registers, Genexpert machines and GPS co- version10.5 for visual analysis of MTB cases. The ordinates captured for the 308 requesting health study used MTB detected frequencies at a facility in facility. Unit used for recording coordinates was relation to surrounding facilities to run optimised requesting facility. hotspot analysis function in Arc Map 10.5 to implement the Gi* statistic. The Getis-Ord (Gi*) statistic assesses the extent to which events such as MTB data exhibit identifiable spatial patterns in space as hotspots and cold spots. Hotspots occur when areas with high values are surrounded by high MTB values while cold spots are locations with typically low values surrounded by similarly low values, from a given location i at spatially varying distances. The optimised hotspot analysis function in Arc Map 10.5 was used to implement the Gi* statistic. The selection of the optimised hotspot analysis was based on its ability to correct for multiple testing as well as spatial dependence. The Z-scores and p-values measure statistical significance, influencing the decision whether to reject or fail to reject the null hypothesis. A high z- score and small p-value for a feature indicates a spatial clustering of high values. A low negative z- Figure1: Districts of Manicaland Province. score and small p-value indicates a spatial clustering of low values. The higher the z-score, the more intense the clustering.
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